A system for predicting energy consumption by a vehicle during a trip has a controller adapted to receive a route plan for the trip. The controller has a processor and tangible, non-transitory memory on which instructions are recorded. The system has a plurality of machine learning modules, including a behavior predictor, a driving consumption predictor and an auxiliary consumption predictor. The controller is adapted to predict a total energy consumed by the vehicle during the trip by executing the plurality of machine learning modules. The driving consumption predictor is adapted to predict a primary energy consumed for propelling the vehicle based in part on the route plan. The auxiliary consumption predictor is adapted to predict a secondary energy consumed by the vehicle for non-propulsion purposes. The behavior predictor is adapted to predict an average vehicle speed based at least partially on traffic conditions.
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2. The system of claim 1, wherein the plurality of machine learning modules includes at least two types of machine learning models.
3. The system of claim 1, wherein the auxiliary consumption predictor includes an HVAC sub-module adapted to predict the secondary energy consumed by the vehicle for heating, cooling and ventilation purposes.
4. The system of claim 1, wherein the auxiliary consumption predictor includes a battery thermal conditioning sub-module adapted to predict the secondary energy consumed for thermal conditioning.
6. The system of claim 1, wherein the first set of features includes traffic data, time data and weather data, with the traffic data including live speed, speed limits and historical speed, the time data including a time of day and a day of week, and the weather data including ambient temperature, wind speed and wind direction.
8. The system of claim 1, wherein the predicted average vehicle speed (AVS) is obtained as: [AVS=a+b*ATS+c*ATS2], where the average traffic speed is ATS, the squared value of the average traffic speed is ATS2 and the set of learned coefficients is (a, b, c).
9. The system of claim 1, wherein the feature extractor is adapted to extract a second set of features, the driving consumption predictor being adapted to receive the second set of features and the predicted average vehicle speed generated by the behavior predictor.
10. The system of claim 9, wherein the auxiliary consumption predictor is adapted to receive the second set of features and wherein the total energy consumed by the vehicle during the trip is obtained by adding the primary energy and the secondary energy.
11. The system of claim 9, wherein the second set of features includes route data and weather data, the route data including respective values of latitude, longitude, elevation, length and road quality of the trip segments and the weather data including ambient temperature, wind speed and wind direction.
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February 1, 2022
September 10, 2024
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